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Article dans une revue

Skull Segmentation and Reconstruction From Newborn CT Images Using Coupled Level Sets

Abstract : This study presents a new approach for segmentation and reconstruction of newborn's skull including bones, fontanels, and sutures from computed tomography (CT) images. The segmentation approach relies on propagation of a pair of interacting smooth surfaces based on geodesic active regions. These surfaces evolve in opposite directions; the exterior surface moves inward while the interior one moves in outward direction. The moving surfaces are forced to stop when arriving at the outer or the inner surface of the cranial bones using edge information. Since fontanels and sutures are not directly detectable in CT images, this method imposes specific propagation constraints for coupled interfaces to prevent the moving surfaces from intersecting each other and penetrating into the opposite region. Finally, an algorithm for level set initialization is introduced which enforces the evolving surfaces to conform to the shape of the head. The proposed method was evaluated using 18 neonatal CT images. The segmentation results achieved by the suggested method have been compared with manual segmentations by two different raters, performed to establish a reliable reference. The comparison of the two segmentation results using the Dice similarity coefficient and modified Hausdorff distance shows that the proposed approach provides satisfactory results.
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Soumis le : mercredi 9 mars 2022 - 18:36:36
Dernière modification le : mardi 6 septembre 2022 - 17:10:22




Sona Ghadimi, Hamid Abrishami Moghaddam, Reinhard Grebe, Fabrice Wallois. Skull Segmentation and Reconstruction From Newborn CT Images Using Coupled Level Sets. IEEE Journal of Biomedical and Health Informatics, Institute of Electrical and Electronics Engineers, 2016, 20 (2), pp.563-573. ⟨10.1109/JBHI.2015.2391991⟩. ⟨hal-03603397⟩



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